Adversarial Machine Learning

Related lectures (29)

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Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Analyzes gradient descent on two-layer ReLU neural networks, exploring global convergence, regularization, implicit bias, and statistical efficiency.

Introduces Convolutional Neural Networks, explaining their architecture, training process, and applications in semantic segmentation tasks.

Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.

Explores double descent curves and overparametrization in machine learning models, highlighting the risks and benefits.

Explores adversarial training optimization, practical implementation, interpretability, fairness, Wasserstein distance, and Wasserstein GANs.

Covers the fundamentals of multilayer neural networks and deep learning.

Provides insights on gradient-based algorithms, deep learning mysteries, and the challenges of non-convex problems.